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import torch
from typing import List
import numpy as np
from PIL import Image


def numpy_to_pil(images: np.ndarray) -> List[Image.Image]:
    """
    Convert a numpy image or a batch of images to a PIL image.

    Args:
        images (np.ndarray): The image array to convert to PIL format.

    Returns:
        List[Image.Image]: A list of PIL images.
    """
    if images.ndim == 3:
        images = images[None, ...]
    images = (images * 255).round().astype("uint8")
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images]
    else:
        pil_images = [Image.fromarray(image) for image in images]

    return pil_images


def merge_tensor_by_mask(tensor_1, tensor_2, mask, dim):
    assert tensor_1.shape == tensor_2.shape
    # Mask is a 0/1 vector. Choose tensor_2 when the value is 1; otherwise, tensor_1
    masked_indices = torch.nonzero(mask).squeeze(1)
    tmp = tensor_1.clone()
    if dim == 0:
        tmp[masked_indices] = tensor_2[masked_indices]
    elif dim == 1:
        tmp[:, masked_indices] = tensor_2[:, masked_indices]
    elif dim == 2:
        tmp[:, :, masked_indices] = tensor_2[:, :, masked_indices]
    return tmp
